Microsoft Visual Studio Professional 2026 bundle pairs dev tools with courses in Python, JavaScript, and SQL.
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Moonshot AI’s Kimi K3 topped a frontend coding benchmark, beating Claude Fable 5 while adding pressure on US AI leaders.
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There are a lot of AI coding applications out there, and as impressive as large language models and the agents they enable have become, many of the most recent developments in AI-assisted development have been in the software that manages those models, not just the models themselves.
Earlier this summer, I spoke with the head of product for Claude Code, Anthropic's Cat Wu, about that company's approach to building that software.
© Augment Code
The widespread introduction of AI-powered coding tools has led to some dramatic splits between those integrating those tools into their workflows and anti-AI absolutists who don't want large language model-generated code anywhere near their projects. When it comes to the Linux kernel, though, creator and top-level maintainer Linus Torvalds said he is "willing to absolutely put my foot down" in support of using AI tools to improve the long-standing open source project.
Writing in a lengthy post on the Linux kernel mailing list this week, Torvalds said that "Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away."
The statement came amid a lengthy thread arguing about the use of Sashiko, an "agentic Linux kernel code review system" that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. But the tool can also waste maintainers' time by sending "false positive" reports of bugs that don't exist, at a rate Sashiko's maintainers estimate is "well within [the] 20% range."
© Getty Images
Last month, we passed along Modern Vintage Gamer's (MVG) confident assertion that Doom is functionally impossible to run on the Neo Geo, owing to the console's sprite-based display hardware and lack of a frame buffer. We all should have known better than to tell a dedicated group of hackers that something is "impossible," though, as two recent projects have made great progress toward functional Doom ports on stock Neo Geo hardware.
Both of these projects have significant graphical compromises that limit how viable they would have been for a marketable, '90s-era console port, as MVG lays out in a new video. Still, they stand as a testament to the surprising results that clever, determined coders can coax out of legacy hardware.
To create the Doom64KB project for the Neo Geo, coder FrenkelS adapted an earlier Doom port they designed to run on 16-bit PC processors like the 8088 and 286. Using that engine, the Neo Geo code then makes a kind of proto frame buffer out of the console's fix layer, an area of display memory that's usually used to display menus and HUD information on top of gameplay.
© MVG / Doom-NG
MicroPython, for the uninitiated, is a pared-down version of python meant to run on today’s powerful microcontollers. As impressive as it was for its day, the SNES is not quite in their league in terms of computing power. Time marches on, and so while there may be other indie releases worth mentioning, we’re declaring the hottest SNES game this season to be [Fabian Kübler]’s port of MicroPython.
Well, except he didn’t exactly do the porting himself: the Antrhopic LLM Claude generated the code, and performed most of the testing, as [Fabian]’s test of its new Fable 5 model. A brief pause during an export ban showed that Opus would crash and burn on the same task, but Fable was able to get things quickly back on track. It might be “AI slop” by some definitions, but the port scales 430 out of 468 on MicroPython’s core test/basics, which makes it usable to play some simple python games… slowly.
As you can see for yourself in an embedded emulator if you check out [Fabian]’s blog, spooling up MicroPython takes about twenty seconds at 3.58 MHz, and after that you can watch some sprites bouncing around at a blistering 0.8 FPS. [Fabian] seems satisfied with that performance, and impressed with Fable’s efforts at optimization. What to you think? Does the hardware have much more to give, or is that about it, given the nature of the Pythonic beast? Perhaps some plucky human could become a digital John Henry by producing a better, faster port — if you do, please let us know. If you’d rather just to see what Fable can do, the project is available on GitHub, so you can judge for yourself how sloppy the code is or test out the ROM.
Putting Python onto limited hardware may not to be to everyone’s taste, but there’s a good case to be made for it. The SNES may actually be too limited, though. It makes sense — the kind of micros you run MicroPython on can emulate the SNES.
On this week’s show, we’re on the GeekWire deck for our annual founder open house, where we dig into Microsoft’s latest round of layoffs — including a major Xbox shakeup — and the surprising rise of hardware companies on the GeekWire 200.
Then we sit down with four guests to talk about how AI is reshaping how they build:
Finally, this week’s GeekWire Trivia Challenge: how a longtime T-Mobile executive got his start in the wireless business, and the star-studded history of T-Mobile celebrity endorsers.
Stories mentioned:
Audio editing by Curt Milton.
[Simon Willison] ported the Moebuis 0.2B image inpainting model to run locally in a web browser. The web tool simply requires a user to provide an image, mark a section of it to be removed, and the model will do it’s best to patch up the missing area. The project was handled by Claude Code as an experiment in how things in the AI coding world have evolved, but more on that in a moment.
The existence of this tool shows that it’s possible for this kind of image editing to be done on the client side, running entirely locally with no reliance on remote services or server-side GPU resources. The online demo (GitHub repository here) is available if you want to try it out, but be warned it triggers a 1.27 gigabyte download of the required model on the first run.
What’s also interesting is [Simon]’s write-up, because he used the project as an opportunity to learn what has changed in the realm of AI coding agents. [Simon] is a software developer but in this project he didn’t personally write any of the code. One may think that means he didn’t learn anything other than how to use the tools, but that’s not quite true.
He learned it’s possible to convert a PyTorch-based model to ONXX, that the converted model can run in supported browsers using local WebGPU acceleration, and that the CacheStorage API will work on large files. Last but not least, he learned Claude Opus 4.8 is capable of handling such a project pretty much autonomously, and even created an informative document explaining the underlying architecture.
One may consider AI coding agents to be disasters waiting to happen, but it’s also true that the landscape is changing quickly, and write-ups like [Simon]’s give a helpful peek at those developments.
Bill Colleran, a veteran technology executive who previously led Impinj and sold Innovent Systems to Broadcom, has joined Seattle-based AI coding startup Adronite as CEO.
Edward Rothschild, who co-founded Adronite in 2023 and served as its first CEO, is transitioning to chief technology officer, where he’ll continue leading the company’s product development, including its Adronite Context Engine and Codistry AI code generation tool, according to a news release.
The 15-person company raised a $5 million Series A led by Gatemore Capital Management earlier this year. The platform supports cloud, on-premises and air-gapped deployments, targeting midmarket companies and regulated industries.
Colleran has more than 35 years of experience in semiconductor and enterprise technology. He grew Impinj into a market leader in RFID technology, raising more than $100 million in equity financing. He left the company in 2014 and was succeeded by co-founder Chris Diorio.
He was also CEO of Innovent Systems, which developed the world’s first CMOS Bluetooth chip and was acquired by Broadcom for approximately $500 million.
More recently he founded lidar company Lumotive and led Seattle SaaS startup AnswerDash. He holds a Ph.D. in electrical engineering from UCLA and a J.D. from Harvard Law School.
“Throughout my career, I’ve seen technology industries transformed when complexity becomes manageable,” Colleran said in a statement. “Software development now faces a similar challenge. AI can generate code at an incredible pace, but understanding complex software systems remains difficult for both developers and AI.”
Adronite’s platform aims to help developers and AI agents understand entire codebases rather than working file by file — a challenge especially acute for midmarket companies managing legacy systems without the tooling available to large enterprises.
The company says its approach can cut token consumption by up to 40%, a claim that could resonate as engineering teams grapple with rising AI costs.
Like so many large and popular open source projects these days, the Godot game engine struggles with an influx of pull requests. The situation has become increasingly dire due to the advent of AI-generated code. More specifically, the issue involves the inverse relationship between PR code quality and the number of PRs, which wastes a lot of time on the side of a limited number of (volunteer) reviewers. This has now forced the project to update its contribution policy.
An interesting point raised in the announcement article is that of the demoralizing effect of AI-generated PRs on reviewers. Often the human behind such a PR isn’t interested in being educated, or may even be an automated agent which isn’t capable of productive discussion on pros and cons of certain coding approaches — never mind in becoming a more permanent maintainer for the project.
This problem has led to new rules being instated, which include a ban on autonomous AI agents and vibe coding, a ban on substantial AI generating of code, and a ban on AI-generated text in human-to-human communication. It also codifies the requirement that all PRs are to be reviewed and approved by a human being before merging.
In many ways this new policy is similar to that of the Mesa project, which demands code comprehension on the side of the submitter, although it doesn’t go as far as NetBSD, which just outright treats LLM-generated code as ‘tainted’ due to potential licensing and other concerns. Other projects like the Linux kernel opt to make the human submitter responsible for any AI tool usage by forcing them to declare it.
Meanwhile there are also indications that such ‘AI tool’ usage is reducing useful interactions with open source projects. What the future will bring here remains to be seen, but at least as far as open source projects go these tools are clearly increasingly being banished.
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